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Sentiment Patterns in Videos from Left- and Right-Wing Youtube News Channels

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Sentiment Patterns in Videos from Left- and Right-Wing Youtube News Channels Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels Felix Soldner1, Justin Chun-ting Ho2, Mykola Makhortykh3, Isabelle van der Vegt1, Maximilian Mozes1;4 and Bennett Kleinberg1;3 1University College London 2University of Edinburgh 3University of Amsterdam 4Technical University of Munich 1{felix.soldner, isabelle.vandervegt, bennett.kleinberg}@ucl.ac.uk [email protected], [email protected], [email protected] Abstract Netherlands) (Newman et al., 2018). The reasons for the growing online consumption of news are News consumption exhibits an increasing shift many: digital outlets provide higher interactivity towards online sources, which bring platforms such as YouTube more into focus. Thus, the and greater freedom of choice for news readers distribution of politically loaded news is eas- (Van Aelst et al., 2017), and the possibility to ac- ier, receives more attention, but also raises the cess them through mobile devices enables more concern of forming isolated ideological com- flexibility and convenience in consuming news munities. Understanding how such news is content (Schrøder, 2015). communicated and received is becoming in- Despite the multiple benefits associated with creasingly important. To expand our under- standing in this domain, we apply a linguistic "the digital turn" in news consumption, it also temporal trajectory analysis to analyze senti- raises concerns regarding possible changes in the ment patterns in English-language videos from societal role of news media (Coddington, 2015). news channels on YouTube. We examine tran- It has been established that there is a relationship scripts from videos distributed through eight between news consumption and the formation of channels with pro-left and pro-right political public agendas, including political attitudes of the leanings. Using unsupervised clustering, we general population (McCombs, 2014). However, identify seven different sentiment patterns in the possible impact of digitalization on these re- the transcripts. We found that the use of two sentiment patterns differed significantly de- lationships remains a subject of academic debate pending on political leaning. Furthermore, we (Helberger, 2015; Eskens et al., 2017; Van Aelst used predictive models to examine how differ- et al., 2017). An increasing number of stud- ent sentiment patterns relate to video popular- ies examine the possible connection between on- ity and if they differ depending on the chan- line news consumption and the formation of iso- nel’s political leaning. No clear relations be- lated ideological communities, which can nur- tween sentiment patterns and popularity were ture biases and limit citizens’ societal participa- found. However, results indicate, that videos from pro-right news channels are more pop- tion (Gentzkow and Shapiro, 2011; Flaxman et al., ular and that a negative sentiment further in- 2016; Sunstein, 2017). Such formations can lead creases that popularity, when sentiments are to increased political or personal radicalization, averaged for each video. which is a concern for societal cohesion. Keywords: linguistic temporal trajectory Polarization concerns related to audience seg- analysis, online news, left-wing, right-wing, mentation are amplified by the use of affective lan- sentiment analysis, YouTube guage for producing online news stories (Soroka et al., 2015). Despite the widespread belief that 1 Introduction news stories tend to use balanced and unbiased Today, news is increasingly consumed through on- language, recent studies suggest that this is not line platforms. Approximately nine out of ten necessarily true, in particular in the case of news adults (93%) in the US acknowledge reading news stories crafted for online environments such as online (Center, 2018); similar, if slightly lower news websites (Young and Soroka, 2012). A num- rates, are observed for many European coun- ber of studies suggest that the use of affective tries (e.g. 65% of Germany and 79% in the language can increase audience engagement with 84 Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science, pages 84–93 Minneapolis, Minnesota, June 6, 2019. c 2019 Association for Computational Linguistics news content (e.g. stories with a negative tone The growing adoption of sentiment analysis as seem to be more popular (Trussler and Soroka, a research tool is accompanied by methodologi- 2014; Soroka et al., 2015)). At the same time, the cal improvements. Originally employed for clas- relationship between a narrative’s popularity and sifying user reviews coming from the commercial the specific patterns of sentiment remains under- domain (Pang and Lee, 2008), early approaches investigated, as well as the ways this relation- to sentiment analysis were focused on producing a ship varies among audiences with different politi- single sentiment score for the specific document or cal leanings. a text section using binary assessments of polarity. In this paper, we contribute to the debate on Such approaches, however, resulted in rather sim- the use of emotionally loaded language in on- plified evaluations, which reduced sentiment com- line news stories as well as its variations between plexity to binary constructs under which the whole outlets with different political leanings. Specif- document could be either positive or negative or ically, we examine the sentiment trajectories of (in some cases) neutral. This can lead to incorrect YouTube news channels related to major English- conflations of sentiment variations within a text. language media outlets. Our interest in YouTube The response to these limitations included the is attributed to the platform’s significant impact advancement towards more fine-grained assess- on information consumption as users increasingly ments of sentiment, including the identification of consume news in the video format (al Nashmi a more complex spectrum of emotions (Cambria et al., 2017). Together with Facebook, YouTube et al., 2015), but also the transition towards the dy- is the main platform used by consumers to watch namic assessment of sentiment shifts throughout news outside of news organizations’ own websites texts (Jockers, 2015; Tanveer et al., 2018; Klein- (Newman et al., 2018). News content produced by berg et al., 2018). news organizations for YouTube usually follows Until now, the sentiment of news stories re- traditional broadcast standards (Peer and Ksiazek, mains a rather under-investigated subject. Kaya 2011) and formats (al Nashmi et al., 2017) and et al.(2012) note that unlike user reviews of prod- often serves as an extension of their distribution ucts (e.g. movies), news stories are considered model. At the same, YouTube also enables ex- to be written in a neutral way. A different study perimenting with new formats and engaging au- found that news stories about the economy and diences in more interactive or provocative ways the environment were overall more positive than (al Nashmi et al., 2017). about crime or international topics, which were Using YouTube as a case platform, we test for overall more negative (Young and Soroka, 2012). differences in the dynamic use of sentiment in Despite the growing number of studies on news news coverage between channels leaning to the sentiment, only a few of them so far approach it political left (e.g. CNN) and right (e.g. Fox by considering the dynamic shifts of sentiment. News). Additionally, we examine if sentiment pat- A study, examining sentiments of different top- terns have predictive value for news videos’ popu- ics (e.g. earthquakes) included time stamps over larity on YouTube. a three months period, to model the sentimental change of news stories (Fukuhara et al., 2007). 2 Related Work In that way, they were able to show a sudden in- Since the mid 2000s, sentiment analysis remains crease of negative emotions in news corpora, when one of the fastest growing fields of machine learn- an earthquake occurred. These negative emotions ing and computational linguistics (Mäntylä et al., slowly decreases over time. While this approach 2018). Defined by Liu(2010) as a collection of models sentiment change over time, it cannot ac- methods for detecting and extracting subjective count for shifts within single stories. information (e.g. opinions, attitudes and emo- Among multiple areas of research on news story tions) from language, sentiment analysis is in- sentiment, the issue of variation in the use of affec- creasingly adopted for multiple academic areas, tive language by news outlets with different politi- varying from from media studies (Sivek, 2018) cal leanings (e.g. left- or right-wing) is both under- to literature (Gao et al., 2016) to political sci- investigated and urgent. The language of politics- ence (Cambria, 2016) and conflict studies (Welch, related texts does not only reflect, but can also 2018). influence the sentiments of the audience (Brader, 85 2005). A number of studies point to the dis- 3.1 News channels selection tinct features of online political communication The data consist of all English-language chan- depending on the actors’ political leaning (e.g. nels from the top 250 news channels on (Engesser et al., 2017; Bracciale and Martella, YouTube, which were ranked by SOCIAL- 2017; Hameleers et al., 2017; Schoonvelde et al., BLADE3 (www.socialblade.com, retrieved 2019a)). Engesser et al.(2017) identify that social November 2018). From that pool, 18 news chan- media usage of right-wing political actors is char- nels were selected, which were identified as the acterized by the use of a few key features, such as ones holding political bias (i.e. either left- or emphasizing the sovereignty of the people, advo- right-wing) by Media Bias/Fact Check4. The web- cating for the people, attacking the elite, ostraciz- site uses a rating method to identify biases among ing others, and invoking the concept of a "heart- information sources and has been used by pre- land". Similarly, Bracciale and Martella(2017) vious studies dealing with media bias (Bentley show that communication styles of populist actors et al., 2019; Bovet and Makse, 2019; Mehta and tend to involve highly emotional language and that Guzmán, 2018).
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